Methods for preprocessing time and distance series data from personal monitoring devices

摘要

There is a need to develop more advanced tools to improve guidance on physical exercise to reduce risk of adverse events and improve benefits of exercise. Vast amounts of data are generated continuously by Personal Monitoring Devices (PMDs) from sports events, biomedical experiments, and fitness self-monitoring that may be used to guide physical exercise. Most of these data are sampled as time- or distance-series. However, the inherent high-dimensionality of exercise data is a challenge during processing. As a result, current data analysis from PMDs seldomly extends beyond aggregates.